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      "/home/zlh/.conda/envs/gfs2025/lib/python3.10/site-packages/pyproj/network.py:59: UserWarning: pyproj unable to set PROJ database path.\n",
      "  _set_context_ca_bundle_path(ca_bundle_path)\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (time: 1, ens_member: 10, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 2021-03-26\n",
       "  * ens_member        (ens_member) int64 0 1 2 3 4 5 6 7 8 9\n",
       "  * lat               (lat) float32 60.0 59.75 59.5 59.25 ... 0.75 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 70.0 70.25 70.5 70.75 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, ens_member, lat, lon) float64 ...\n",
       "    target_time       (time) datetime64[ns] ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-92b0249b-73e3-4d71-b841-270d958593a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92b0249b-73e3-4d71-b841-270d958593a0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>ens_member</span>: 10</li><li><span class='xr-has-index'>lat</span>: 241</li><li><span class='xr-has-index'>lon</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-566be5ab-11b7-4f98-81bb-41129698d46b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-566be5ab-11b7-4f98-81bb-41129698d46b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-26</div><input id='attrs-233e15f7-4db2-4f81-9766-4d3bce6cf20c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-233e15f7-4db2-4f81-9766-4d3bce6cf20c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-953a81fd-6ed2-4a42-9a15-52827b7a7194' class='xr-var-data-in' type='checkbox'><label for='data-953a81fd-6ed2-4a42-9a15-52827b7a7194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-26T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ens_member</span></div><div class='xr-var-dims'>(ens_member)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-e8988c17-5da6-4388-a486-19907b950f8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8988c17-5da6-4388-a486-19907b950f8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a4429f3-40b5-48b2-8865-480cf01a28e5' class='xr-var-data-in' type='checkbox'><label for='data-8a4429f3-40b5-48b2-8865-480cf01a28e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 0.5 0.25 0.0</div><input id='attrs-2216cae7-7d31-49f6-b2f8-0f6d0efbf1e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2216cae7-7d31-49f6-b2f8-0f6d0efbf1e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a467e3e-7c57-424c-b7b6-95764dfa9522' class='xr-var-data-in' type='checkbox'><label for='data-7a467e3e-7c57-424c-b7b6-95764dfa9522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , ...,  0.5 ,  0.25,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 70.25 70.5 ... 139.8 140.0</div><input id='attrs-1e43b445-54b8-4dc7-a661-dec5e39c515f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e43b445-54b8-4dc7-a661-dec5e39c515f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f340d436-7633-4dfe-a427-5609c257b78d' class='xr-var-data-in' type='checkbox'><label for='data-f340d436-7633-4dfe-a427-5609c257b78d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 70.  ,  70.25,  70.5 , ..., 139.5 , 139.75, 140.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6534edf7-bebd-4eef-a5d4-42f9ffb7127d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6534edf7-bebd-4eef-a5d4-42f9ffb7127d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>A_PCP_L1_Accum_1</span></div><div class='xr-var-dims'>(time, ens_member, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26733170-4507-4c32-bcca-7c3399caf31c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26733170-4507-4c32-bcca-7c3399caf31c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-664d7eab-2ca4-480a-a723-aab1640235b7' class='xr-var-data-in' type='checkbox'><label for='data-664d7eab-2ca4-480a-a723-aab1640235b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[677210 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3dfac833-8c5d-4f2b-9cd5-b3c28e06f2f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3dfac833-8c5d-4f2b-9cd5-b3c28e06f2f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e7e060-2e03-481f-a5d9-320e7de1906d' class='xr-var-data-in' type='checkbox'><label for='data-53e7e060-2e03-481f-a5d9-320e7de1906d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64a9bb8f-2d1a-4b07-b443-c5fd57458ca4' class='xr-section-summary-in' type='checkbox'  ><label for='section-64a9bb8f-2d1a-4b07-b443-c5fd57458ca4' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f18b0a56-48bc-4420-8850-dd7e4b47d537' class='xr-index-data-in' type='checkbox'/><label for='index-f18b0a56-48bc-4420-8850-dd7e4b47d537' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-03-26&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ens_member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31097b3b-2cd7-4c76-9664-4af138746451' class='xr-index-data-in' type='checkbox'/><label for='index-31097b3b-2cd7-4c76-9664-4af138746451' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;ens_member&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-07ec0469-4fe7-4bd1-b6da-0aa5c7505e45' class='xr-index-data-in' type='checkbox'/><label for='index-07ec0469-4fe7-4bd1-b6da-0aa5c7505e45' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,   0.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-55a563bc-1383-4e29-9e24-87b72cbe128e' class='xr-index-data-in' type='checkbox'/><label for='index-55a563bc-1383-4e29-9e24-87b72cbe128e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  70.0,  70.25,   70.5,  70.75,   71.0,  71.25,   71.5,  71.75,   72.0,\n",
       "        72.25,\n",
       "       ...\n",
       "       137.75,  138.0, 138.25,  138.5, 138.75,  139.0, 139.25,  139.5, 139.75,\n",
       "        140.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=281))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6838063b-d8dc-40cf-ac6d-3dd98bd5264d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6838063b-d8dc-40cf-ac6d-3dd98bd5264d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (time: 1, ens_member: 10, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 2021-03-26\n",
       "  * ens_member        (ens_member) int64 0 1 2 3 4 5 6 7 8 9\n",
       "  * lat               (lat) float32 60.0 59.75 59.5 59.25 ... 0.75 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 70.0 70.25 70.5 70.75 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, ens_member, lat, lon) float64 ...\n",
       "    target_time       (time) datetime64[ns] ..."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "xr.open_dataset(r\"/home/zlh/raindall_gfs_revise/data/minio/2021_3_standard/24/gfs.0p25.2021030118.f024.grib2.nc\")\n",
    "\n",
    "data=xr.open_dataset('/home/zlh/raindall_gfs_revise/reference/gfs.t00z.pgrb2.0p25.f024.nc')\n",
    "data\n",
    "#data['mask'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f81f01c1e40>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=xr.open_dataset('/home/zlh/gfs_revise/data/minio/gfs_standard/24/gfs.0p25.2025030818.f024.grib2.nc')\n",
    "data['A_PCP_L1_Accum_1'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新的掩膜文件已生成，保存为 mask_pearl_river_new.nc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f81faaf90f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "# 读取GFS数据文件获取坐标信息\n",
    "data = xr.open_dataset('/home/zlh/gfs_revise/data/minio/202103_standard/24/gfs.0p25.2021030100.f024.grib2.nc')\n",
    "gfs_lats = data.lat.values\n",
    "gfs_lons = data.lon.values\n",
    "\n",
    "# 创建新的掩膜数据，所有点都设置为1\n",
    "new_mask = np.ones((len(gfs_lats), len(gfs_lons)))\n",
    "\n",
    "# 创建新的xarray数据集\n",
    "new_mask_da = xr.DataArray(\n",
    "    new_mask,\n",
    "    dims=['lat', 'lon'],\n",
    "    coords={\n",
    "        'lat': gfs_lats,\n",
    "        'lon': gfs_lons\n",
    "    },\n",
    "    name='mask'\n",
    ")\n",
    "\n",
    "# 保存新的掩膜文件\n",
    "new_mask_da.to_netcdf('/home/zlh/gfs_revise/reference/mask_pearl_river_new.nc')\n",
    "\n",
    "# 显示结果\n",
    "print(\"新的掩膜文件已生成，保存为 mask_pearl_river_new.nc\")\n",
    "new_mask_da.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zlh/.conda/envs/gfs2025/lib/python3.10/site-packages/pyproj/network.py:59: UserWarning: pyproj unable to set PROJ database path.\n",
      "  _set_context_ca_bundle_path(ca_bundle_path)\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "from typing import Optional\n",
    "\n",
    "def encodings(vars: list) -> dict:\n",
    "    \"\"\"\n",
    "    return a dict for saving nc file\n",
    "    Args:\n",
    "        vars: list of names of variables\n",
    "\n",
    "    Returns:\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    return dict.fromkeys(vars, dict(zlib = True))\n",
    "def grb2_to_nc(grb2_path: str, nc_path: str, var_names: Optional[list] = None) -> None:\n",
    "    \"\"\"\n",
    "    将GRIB2文件转换为NetCDF格式\n",
    "    \n",
    "    Args:\n",
    "        grb2_path: GRIB2文件路径\n",
    "        nc_path: 输出的NetCDF文件路径\n",
    "        var_names: 需要保存的变量名列表，如果为None则保存所有变量\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 直接使用xarray读取GRIB2文件\n",
    "        ds = xr.open_dataset(grb2_path, engine='cfgrib', backend_kwargs={'read_keys': ['typeOfLevel', 'level', 'stepRange']})\n",
    "        \n",
    "        # 如果指定了变量名，则只保留指定的变量\n",
    "        if var_names is not None:\n",
    "            ds = ds[var_names]\n",
    "        \n",
    "        # 设置压缩编码\n",
    "        encoding = encodings(list(ds.data_vars))\n",
    "        \n",
    "        # 保存为NetCDF文件\n",
    "        ds.to_netcdf(nc_path, encoding=encoding)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"转换失败：{str(e)}\")\n",
    "    finally:\n",
    "        # 确保数据集被正确关闭\n",
    "        try:\n",
    "            ds.close()\n",
    "        except:\n",
    "            pass\n",
    "grb2_to_nc(\n",
    "    grb2_path='/home/zlh/raindall_gfs_revise/reference/gfs.t00z.pgrb2.0p25 (1).f024',\n",
    "    nc_path='/home/zlh/raindall_gfs_revise/reference/gfs.t00z.pgrb2.0p25.f024.nc',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
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       "  font-weight: normal;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 273kB\n",
       "Dimensions:           (time: 1, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 8B 2021-03-01\n",
       "  * lat               (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, lat, lon) float32 271kB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-50167217-46e5-4059-8814-6eb61932d48e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-50167217-46e5-4059-8814-6eb61932d48e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 241</li><li><span class='xr-has-index'>lon</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2622dbac-0ae3-4439-8c0b-18610c4d28d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2622dbac-0ae3-4439-8c0b-18610c4d28d6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-01</div><input id='attrs-6d17e680-d772-4ef0-9c9e-0d58e424450a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d17e680-d772-4ef0-9c9e-0d58e424450a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2fbedd6-3702-4250-9603-5d4c012331ef' class='xr-var-data-in' type='checkbox'><label for='data-f2fbedd6-3702-4250-9603-5d4c012331ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 0.5 0.25 0.0</div><input id='attrs-216434c6-7c41-4feb-9036-830d743d7979' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-216434c6-7c41-4feb-9036-830d743d7979' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-606a5299-6c07-46b9-82de-a5e632170b5b' class='xr-var-data-in' type='checkbox'><label for='data-606a5299-6c07-46b9-82de-a5e632170b5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , ...,  0.5 ,  0.25,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 70.25 70.5 ... 139.8 140.0</div><input id='attrs-5c26f2f9-4369-42e7-a198-1ae32c5dbd13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c26f2f9-4369-42e7-a198-1ae32c5dbd13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-544757be-1394-403b-86b7-8e4bc9d3c086' class='xr-var-data-in' type='checkbox'><label for='data-544757be-1394-403b-86b7-8e4bc9d3c086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 70.  ,  70.25,  70.5 , ..., 139.5 , 139.75, 140.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68ce3047-a7fe-4b54-bcc7-dea2126b824a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68ce3047-a7fe-4b54-bcc7-dea2126b824a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>A_PCP_L1_Accum_1</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a71ada7d-6739-464e-984c-49ddd10fd1da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a71ada7d-6739-464e-984c-49ddd10fd1da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e029e94-b006-4682-8045-d8953eca7289' class='xr-var-data-in' type='checkbox'><label for='data-8e029e94-b006-4682-8045-d8953eca7289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>level :</span></dt><dd>Ground or water surface</dd><dt><span>cell_methods :</span></dt><dd>time: sum over hours</dd></dl></div><div class='xr-var-data'><pre>[67721 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-738e0667-e567-41a8-83a0-b606a7a1f633' class='xr-section-summary-in' type='checkbox'  ><label for='section-738e0667-e567-41a8-83a0-b606a7a1f633' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-262f66c9-c40e-43a2-9a7d-45f5a2d2a3c2' class='xr-index-data-in' type='checkbox'/><label for='index-262f66c9-c40e-43a2-9a7d-45f5a2d2a3c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-03-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0f5987d0-1093-415b-af80-c7be9385c6ab' class='xr-index-data-in' type='checkbox'/><label for='index-0f5987d0-1093-415b-af80-c7be9385c6ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,   0.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1082c009-79c4-4a82-8cdc-8ffbed5216f2' class='xr-index-data-in' type='checkbox'/><label for='index-1082c009-79c4-4a82-8cdc-8ffbed5216f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  70.0,  70.25,   70.5,  70.75,   71.0,  71.25,   71.5,  71.75,   72.0,\n",
       "        72.25,\n",
       "       ...\n",
       "       137.75,  138.0, 138.25,  138.5, 138.75,  139.0, 139.25,  139.5, 139.75,\n",
       "        140.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=281))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6714c82f-8176-4f15-97a0-f6f037dcbe46' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6714c82f-8176-4f15-97a0-f6f037dcbe46' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 273kB\n",
       "Dimensions:           (time: 1, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 8B 2021-03-01\n",
       "  * lat               (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, lat, lon) float32 271kB ..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "xr.open_dataset('/home/zlh/gfs_revise/data/minio/202103_standard/48/gfs.0p25.2021030100.f048.grib2.nc')\n",
    "#xr.open_dataset('/home/zlh/gfs_revise/reference/gfs.0p25.2021010100.f024.grib2.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 7GB\n",
       "Dimensions:           (time: 124, ens_member: 100, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 992B 2021-01-01 ... 2024-01-31\n",
       "  * ens_member        (ens_member) int64 800B 0 1 2 3 4 5 ... 94 95 96 97 98 99\n",
       "  * lat               (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, ens_member, lat, lon) float64 7GB ...\n",
       "    target_time       (time) datetime64[ns] 992B ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-da3ad6a5-b93a-45ac-afad-0e1207d82c9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da3ad6a5-b93a-45ac-afad-0e1207d82c9b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 124</li><li><span class='xr-has-index'>ens_member</span>: 100</li><li><span class='xr-has-index'>lat</span>: 241</li><li><span class='xr-has-index'>lon</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ae636cea-b18c-4d90-b554-64d3f9307010' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae636cea-b18c-4d90-b554-64d3f9307010' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-01-01 ... 2024-01-31</div><input id='attrs-0c069158-30cc-4756-8352-5f8d03ced911' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c069158-30cc-4756-8352-5f8d03ced911' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14ca2137-6e14-4fea-bce6-509cc4ae4651' class='xr-var-data-in' type='checkbox'><label for='data-14ca2137-6e14-4fea-bce6-509cc4ae4651' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-03T00:00:00.000000000&#x27;, &#x27;2021-01-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-05T00:00:00.000000000&#x27;, &#x27;2021-01-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-07T00:00:00.000000000&#x27;, &#x27;2021-01-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-09T00:00:00.000000000&#x27;, &#x27;2021-01-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-11T00:00:00.000000000&#x27;, &#x27;2021-01-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-13T00:00:00.000000000&#x27;, &#x27;2021-01-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-15T00:00:00.000000000&#x27;, &#x27;2021-01-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-17T00:00:00.000000000&#x27;, &#x27;2021-01-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-19T00:00:00.000000000&#x27;, &#x27;2021-01-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-21T00:00:00.000000000&#x27;, &#x27;2021-01-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-23T00:00:00.000000000&#x27;, &#x27;2021-01-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-25T00:00:00.000000000&#x27;, &#x27;2021-01-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-27T00:00:00.000000000&#x27;, &#x27;2021-01-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-29T00:00:00.000000000&#x27;, &#x27;2021-01-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-31T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-02T00:00:00.000000000&#x27;, &#x27;2022-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-04T00:00:00.000000000&#x27;, &#x27;2022-01-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-06T00:00:00.000000000&#x27;, &#x27;2022-01-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-08T00:00:00.000000000&#x27;, &#x27;2022-01-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-10T00:00:00.000000000&#x27;, &#x27;2022-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-12T00:00:00.000000000&#x27;, &#x27;2022-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-14T00:00:00.000000000&#x27;, &#x27;2022-01-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-16T00:00:00.000000000&#x27;, &#x27;2022-01-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-18T00:00:00.000000000&#x27;, &#x27;2022-01-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-20T00:00:00.000000000&#x27;, &#x27;2022-01-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-22T00:00:00.000000000&#x27;, &#x27;2022-01-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-24T00:00:00.000000000&#x27;, &#x27;2022-01-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-26T00:00:00.000000000&#x27;, &#x27;2022-01-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-28T00:00:00.000000000&#x27;, &#x27;2022-01-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-30T00:00:00.000000000&#x27;, &#x27;2022-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-03T00:00:00.000000000&#x27;, &#x27;2023-01-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-05T00:00:00.000000000&#x27;, &#x27;2023-01-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-07T00:00:00.000000000&#x27;, &#x27;2023-01-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-09T00:00:00.000000000&#x27;, &#x27;2023-01-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-11T00:00:00.000000000&#x27;, &#x27;2023-01-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-13T00:00:00.000000000&#x27;, &#x27;2023-01-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-15T00:00:00.000000000&#x27;, &#x27;2023-01-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-17T00:00:00.000000000&#x27;, &#x27;2023-01-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-19T00:00:00.000000000&#x27;, &#x27;2023-01-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-21T00:00:00.000000000&#x27;, &#x27;2023-01-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-23T00:00:00.000000000&#x27;, &#x27;2023-01-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-25T00:00:00.000000000&#x27;, &#x27;2023-01-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-27T00:00:00.000000000&#x27;, &#x27;2023-01-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-29T00:00:00.000000000&#x27;, &#x27;2023-01-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-31T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-02T00:00:00.000000000&#x27;, &#x27;2024-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-04T00:00:00.000000000&#x27;, &#x27;2024-01-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-06T00:00:00.000000000&#x27;, &#x27;2024-01-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-08T00:00:00.000000000&#x27;, &#x27;2024-01-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-10T00:00:00.000000000&#x27;, &#x27;2024-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-12T00:00:00.000000000&#x27;, &#x27;2024-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-14T00:00:00.000000000&#x27;, &#x27;2024-01-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-16T00:00:00.000000000&#x27;, &#x27;2024-01-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-18T00:00:00.000000000&#x27;, &#x27;2024-01-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-20T00:00:00.000000000&#x27;, &#x27;2024-01-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-22T00:00:00.000000000&#x27;, &#x27;2024-01-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-24T00:00:00.000000000&#x27;, &#x27;2024-01-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-26T00:00:00.000000000&#x27;, &#x27;2024-01-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-28T00:00:00.000000000&#x27;, &#x27;2024-01-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-30T00:00:00.000000000&#x27;, &#x27;2024-01-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ens_member</span></div><div class='xr-var-dims'>(ens_member)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 94 95 96 97 98 99</div><input id='attrs-ad4a1d53-11c1-4f9f-9810-4ba6825e5168' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad4a1d53-11c1-4f9f-9810-4ba6825e5168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba19d8f-8848-4cca-b552-34cd1e9f2a76' class='xr-var-data-in' type='checkbox'><label for='data-3ba19d8f-8848-4cca-b552-34cd1e9f2a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 0.5 0.25 0.0</div><input id='attrs-a8899652-9de4-4445-85fe-8d1139f221e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8899652-9de4-4445-85fe-8d1139f221e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d93ab0da-1c5c-4657-9767-d597c0e32007' class='xr-var-data-in' type='checkbox'><label for='data-d93ab0da-1c5c-4657-9767-d597c0e32007' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>lat</dd><dt><span>long_name :</span></dt><dd>north latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[ 0. 60.]</dd></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , ...,  0.5 ,  0.25,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 70.25 70.5 ... 139.8 140.0</div><input id='attrs-e2e4a081-d0d7-439b-b6fd-938096a52645' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2e4a081-d0d7-439b-b6fd-938096a52645' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e2d873b-7860-4e29-9129-a31292e5078d' class='xr-var-data-in' type='checkbox'><label for='data-0e2d873b-7860-4e29-9129-a31292e5078d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>lon</dd><dt><span>long_name :</span></dt><dd>east longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[ 70. 140.]</dd></dl></div><div class='xr-var-data'><pre>array([ 70.  ,  70.25,  70.5 , ..., 139.5 , 139.75, 140.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-daffae8c-1350-48bd-a4da-cee516851626' class='xr-section-summary-in' type='checkbox'  checked><label for='section-daffae8c-1350-48bd-a4da-cee516851626' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>A_PCP_L1_Accum_1</span></div><div class='xr-var-dims'>(time, ens_member, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b18fa15-804b-48c2-8a8d-0266aca39ce5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b18fa15-804b-48c2-8a8d-0266aca39ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f10710d6-e524-486f-84a8-f325db82c625' class='xr-var-data-in' type='checkbox'><label for='data-f10710d6-e524-486f-84a8-f325db82c625' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[839740400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24d879ca-022c-4ec5-90f6-23f3ce599bb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24d879ca-022c-4ec5-90f6-23f3ce599bb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f69265b6-3325-429e-8d83-b4067269ad54' class='xr-var-data-in' type='checkbox'><label for='data-f69265b6-3325-429e-8d83-b4067269ad54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dca2e3d5-d70a-422a-8564-970f8c67b852' class='xr-section-summary-in' type='checkbox'  ><label for='section-dca2e3d5-d70a-422a-8564-970f8c67b852' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6b1c6841-296c-496e-8a2d-82787634d07a' class='xr-index-data-in' type='checkbox'/><label for='index-6b1c6841-296c-496e-8a2d-82787634d07a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-01-01&#x27;, &#x27;2021-01-02&#x27;, &#x27;2021-01-03&#x27;, &#x27;2021-01-04&#x27;,\n",
       "               &#x27;2021-01-05&#x27;, &#x27;2021-01-06&#x27;, &#x27;2021-01-07&#x27;, &#x27;2021-01-08&#x27;,\n",
       "               &#x27;2021-01-09&#x27;, &#x27;2021-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-01-22&#x27;, &#x27;2024-01-23&#x27;, &#x27;2024-01-24&#x27;, &#x27;2024-01-25&#x27;,\n",
       "               &#x27;2024-01-26&#x27;, &#x27;2024-01-27&#x27;, &#x27;2024-01-28&#x27;, &#x27;2024-01-29&#x27;,\n",
       "               &#x27;2024-01-30&#x27;, &#x27;2024-01-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=124, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ens_member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05ba1d90-0327-43ba-a4c2-e7b390a7641c' class='xr-index-data-in' type='checkbox'/><label for='index-05ba1d90-0327-43ba-a4c2-e7b390a7641c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;ens_member&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44c185f7-31ec-48c2-bcac-17a8f6121d87' class='xr-index-data-in' type='checkbox'/><label for='index-44c185f7-31ec-48c2-bcac-17a8f6121d87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,   0.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c27a8cbd-7623-41d3-aaa1-fa46e9e09e8b' class='xr-index-data-in' type='checkbox'/><label for='index-c27a8cbd-7623-41d3-aaa1-fa46e9e09e8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  70.0,  70.25,   70.5,  70.75,   71.0,  71.25,   71.5,  71.75,   72.0,\n",
       "        72.25,\n",
       "       ...\n",
       "       137.75,  138.0, 138.25,  138.5, 138.75,  139.0, 139.25,  139.5, 139.75,\n",
       "        140.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=281))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf55321c-ec40-41d1-b126-d903abda9933' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf55321c-ec40-41d1-b126-d903abda9933' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 7GB\n",
       "Dimensions:           (time: 124, ens_member: 100, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 992B 2021-01-01 ... 2024-01-31\n",
       "  * ens_member        (ens_member) int64 800B 0 1 2 3 4 5 ... 94 95 96 97 98 99\n",
       "  * lat               (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, ens_member, lat, lon) float64 7GB ...\n",
       "    target_time       (time) datetime64[ns] 992B ..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal = xr.open_dataset(r\"D:\\BC\\Data_experiment\\operational_bgg\\output_v01\\data\\daily_calibration\\daily_calibration_Month01_Lead024.nc\")\n",
    "cal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  * time     (time) datetime64[ns] 992B 2021-01-01 2021-01-02 ... 2024-01-31</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'target_time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 124</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-76395b74-456a-48b3-819a-c7d4511a12d0' class='xr-array-in' type='checkbox' checked><label for='section-76395b74-456a-48b3-819a-c7d4511a12d0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[124 values with dtype=datetime64[ns]]</pre></div></div></li><li class='xr-section-item'><input id='section-63bff852-aebf-4d63-8850-aa70e0130ea1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-63bff852-aebf-4d63-8850-aa70e0130ea1' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-01-01 ... 2024-01-31</div><input id='attrs-c98540ea-c94a-47d4-be4c-e394e0084e81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c98540ea-c94a-47d4-be4c-e394e0084e81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-376174c5-514b-43bc-94e4-52fdc544746e' class='xr-var-data-in' type='checkbox'><label for='data-376174c5-514b-43bc-94e4-52fdc544746e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-02T00:00:00.000000000&#x27;,\n",
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       "               &#x27;2021-01-05&#x27;, &#x27;2021-01-06&#x27;, &#x27;2021-01-07&#x27;, &#x27;2021-01-08&#x27;,\n",
       "               &#x27;2021-01-09&#x27;, &#x27;2021-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-01-22&#x27;, &#x27;2024-01-23&#x27;, &#x27;2024-01-24&#x27;, &#x27;2024-01-25&#x27;,\n",
       "               &#x27;2024-01-26&#x27;, &#x27;2024-01-27&#x27;, &#x27;2024-01-28&#x27;, &#x27;2024-01-29&#x27;,\n",
       "               &#x27;2024-01-30&#x27;, &#x27;2024-01-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=124, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80fdc076-55ec-4d9a-95fe-b1dfa8e23e80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-80fdc076-55ec-4d9a-95fe-b1dfa8e23e80' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'target_time' (time: 124)> Size: 992B\n",
       "[124 values with dtype=datetime64[ns]]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 992B 2021-01-01 2021-01-02 ... 2024-01-31"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "cal['target_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 8MB\n",
       "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "  * param           (param) &lt;U5 80B &#x27;p0&#x27; &#x27;a&#x27; &#x27;loc&#x27; &#x27;scale&#x27;\n",
       "Data variables:\n",
       "    para_fcst       (param, lat, lon) float64 2MB ...\n",
       "    para_obs        (param, lat, lon) float64 2MB ...\n",
       "    corr            (lat, lon) float64 542kB ...\n",
       "    dist_type_fcst  (lat, lon) &lt;U5 1MB ...\n",
       "    dist_type_obs   (lat, lon) &lt;U5 1MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9ac6370a-fdb9-4f83-882d-1e45eda2cd63' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ac6370a-fdb9-4f83-882d-1e45eda2cd63' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>param</span>: 4</li><li><span class='xr-has-index'>lat</span>: 241</li><li><span class='xr-has-index'>lon</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a9c43a35-20b0-4879-9e1e-f68eb4c97550' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9c43a35-20b0-4879-9e1e-f68eb4c97550' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 0.5 0.25 0.0</div><input id='attrs-8a9ad44a-ac2b-47c1-9b96-722cb79ecbb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a9ad44a-ac2b-47c1-9b96-722cb79ecbb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6717644d-b010-4760-a5d0-44ac4c174fac' class='xr-var-data-in' type='checkbox'><label for='data-6717644d-b010-4760-a5d0-44ac4c174fac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>lat</dd><dt><span>long_name :</span></dt><dd>north latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[ 0. 60.]</dd></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , ...,  0.5 ,  0.25,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 70.25 70.5 ... 139.8 140.0</div><input id='attrs-df3d8ab4-99b9-4692-8111-cf0cc7feac6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df3d8ab4-99b9-4692-8111-cf0cc7feac6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46a02fb5-ad65-4747-9bfa-9dbd4f813ebf' class='xr-var-data-in' type='checkbox'><label for='data-46a02fb5-ad65-4747-9bfa-9dbd4f813ebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>lon</dd><dt><span>long_name :</span></dt><dd>east longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[ 70. 140.]</dd></dl></div><div class='xr-var-data'><pre>array([ 70.  ,  70.25,  70.5 , ..., 139.5 , 139.75, 140.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>param</span></div><div class='xr-var-dims'>(param)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;p0&#x27; &#x27;a&#x27; &#x27;loc&#x27; &#x27;scale&#x27;</div><input id='attrs-ea82ae2e-4ad0-4125-90b7-dc6490016924' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea82ae2e-4ad0-4125-90b7-dc6490016924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8785037-0ea2-44dd-922c-acbd8980b049' class='xr-var-data-in' type='checkbox'><label for='data-e8785037-0ea2-44dd-922c-acbd8980b049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;p0&#x27;, &#x27;a&#x27;, &#x27;loc&#x27;, &#x27;scale&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-25f2193b-43ff-41d1-a320-f490350e22ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25f2193b-43ff-41d1-a320-f490350e22ec' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>para_fcst</span></div><div class='xr-var-dims'>(param, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21744607-2046-48bb-a19f-6d0d552bb56d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21744607-2046-48bb-a19f-6d0d552bb56d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23b5961e-8c4e-40db-a044-79e2375ec478' class='xr-var-data-in' type='checkbox'><label for='data-23b5961e-8c4e-40db-a044-79e2375ec478' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[270884 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>para_obs</span></div><div class='xr-var-dims'>(param, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a007190-550a-4b59-a1c4-ff8650538fde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a007190-550a-4b59-a1c4-ff8650538fde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b438a787-575b-4015-ab45-e706b41c1827' class='xr-var-data-in' type='checkbox'><label for='data-b438a787-575b-4015-ab45-e706b41c1827' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[270884 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-605e0a0a-27ce-4e38-8b78-2cabe53eb76a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-605e0a0a-27ce-4e38-8b78-2cabe53eb76a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae87ed14-2a79-4abf-ace4-81bc883755f8' class='xr-var-data-in' type='checkbox'><label for='data-ae87ed14-2a79-4abf-ace4-81bc883755f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[67721 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_type_fcst</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22e05027-204c-499a-a916-93ccdcd3e860' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22e05027-204c-499a-a916-93ccdcd3e860' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-942bb674-1787-40ea-91aa-9c9e387e2205' class='xr-var-data-in' type='checkbox'><label for='data-942bb674-1787-40ea-91aa-9c9e387e2205' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[67721 values with dtype=&lt;U5]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_type_obs</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13027775-d4b9-4849-b2b2-42881eecf748' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13027775-d4b9-4849-b2b2-42881eecf748' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beed0c2e-eca1-45d7-bf42-831fd304b299' class='xr-var-data-in' type='checkbox'><label for='data-beed0c2e-eca1-45d7-bf42-831fd304b299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[67721 values with dtype=&lt;U5]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efc2c971-a4a3-4668-9e22-23a7a5877e7e' class='xr-section-summary-in' type='checkbox'  ><label for='section-efc2c971-a4a3-4668-9e22-23a7a5877e7e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c00cd11d-3c7c-4189-a80b-75cfd800bb61' class='xr-index-data-in' type='checkbox'/><label for='index-c00cd11d-3c7c-4189-a80b-75cfd800bb61' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,   0.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d69f06d9-ec5f-4d3e-9bc5-9bcd356700a0' class='xr-index-data-in' type='checkbox'/><label for='index-d69f06d9-ec5f-4d3e-9bc5-9bcd356700a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  70.0,  70.25,   70.5,  70.75,   71.0,  71.25,   71.5,  71.75,   72.0,\n",
       "        72.25,\n",
       "       ...\n",
       "       137.75,  138.0, 138.25,  138.5, 138.75,  139.0, 139.25,  139.5, 139.75,\n",
       "        140.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=281))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>param</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a79f47d-0a79-4176-a70c-4c2c892fc168' class='xr-index-data-in' type='checkbox'/><label for='index-2a79f47d-0a79-4176-a70c-4c2c892fc168' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;p0&#x27;, &#x27;a&#x27;, &#x27;loc&#x27;, &#x27;scale&#x27;], dtype=&#x27;object&#x27;, name=&#x27;param&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc34ee5b-8819-4899-ad03-e3de50033c97' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc34ee5b-8819-4899-ad03-e3de50033c97' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 8MB\n",
       "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
       "Data variables:\n",
       "    para_fcst       (param, lat, lon) float64 2MB ...\n",
       "    para_obs        (param, lat, lon) float64 2MB ...\n",
       "    corr            (lat, lon) float64 542kB ...\n",
       "    dist_type_fcst  (lat, lon) <U5 1MB ...\n",
       "    dist_type_obs   (lat, lon) <U5 1MB ..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "param = xr.open_dataset(r\"D:\\BC\\Data_experiment\\operational_bgg\\output_v01\\data\\daily_calibration_params\\fitted_params_Month01_Lead024.nc\")\n",
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.64      , 0.61194384, 0.        , 1.2232815 ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param['para_fcst'].sel(lat=53.5, lon=123.25).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "非 NaN 值的数量: 2312 / 67721 (3.41%)\n",
      "非 NaN 值的统计信息:\n",
      "  最小值: 0.8762450954307258\n",
      "  最大值: 0.988675629728019\n",
      "  平均值: 0.9594199163309474\n",
      "\n",
      "前5个非 NaN 值的位置和值:\n",
      "  位置 (纬度=53.5000, 经度=123.2500): 0.9629\n",
      "  位置 (纬度=53.5000, 经度=123.5000): 0.9611\n",
      "  位置 (纬度=53.2500, 经度=121.0000): 0.9529\n",
      "  位置 (纬度=53.2500, 经度=121.2500): 0.9604\n",
      "  位置 (纬度=53.2500, 经度=121.5000): 0.9697\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# 计算非 NaN 值的数量和百分比\n",
    "non_nan_count = np.count_nonzero(~np.isnan(param['corr'].values))\n",
    "total_count = param['corr'].size\n",
    "non_nan_ratio = non_nan_count / total_count\n",
    "print(f\"非 NaN 值的数量: {non_nan_count} / {total_count} ({non_nan_ratio:.2%})\")\n",
    "\n",
    "# 如果有非 NaN 值，显示一些统计信息\n",
    "if non_nan_count > 0:\n",
    "    # 获取非 NaN 值\n",
    "    non_nan_values = param['corr'].values[~np.isnan(param['corr'].values)]\n",
    "    \n",
    "    # 显示统计信息\n",
    "    print(f\"非 NaN 值的统计信息:\")\n",
    "    print(f\"  最小值: {non_nan_values.min()}\")\n",
    "    print(f\"  最大值: {non_nan_values.max()}\")\n",
    "    print(f\"  平均值: {non_nan_values.mean()}\")\n",
    "    \n",
    "    # 显示前几个非 NaN 值的位置和值\n",
    "    non_nan_indices = np.where(~np.isnan(param['corr'].values))\n",
    "    print(\"\\n前5个非 NaN 值的位置和值:\")\n",
    "    for i in range(min(5, len(non_nan_indices[0]))):\n",
    "        lat_idx, lon_idx = non_nan_indices[0][i], non_nan_indices[1][i]\n",
    "        \n",
    "        # 获取实际的经纬度值\n",
    "        lat_value = float(param.lat.values[lat_idx])\n",
    "        lon_value = float(param.lon.values[lon_idx])\n",
    "        \n",
    "        value = param['corr'].values[lat_idx, lon_idx]\n",
    "        print(f\"  位置 (纬度={lat_value:.4f}, 经度={lon_value:.4f}): {value:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 70kB\n",
       "Dimensions:      (lon: 281, lat: 241)\n",
       "Coordinates:\n",
       "    time         datetime64[ns] 8B ...\n",
       "  * lon          (lon) float32 1kB 70.0 70.25 70.5 70.75 ... 139.5 139.8 140.0\n",
       "  * lat          (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.75 0.5 0.25 0.0\n",
       "    spatial_ref  int64 8B ...\n",
       "Data variables:\n",
       "    mask         (lat, lon) bool 68kB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0c7cd670-0454-4ea2-a96d-5cfe396bc9ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c7cd670-0454-4ea2-a96d-5cfe396bc9ce' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 281</li><li><span class='xr-has-index'>lat</span>: 241</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-de834f71-a980-40a1-86f3-15d4c8b828c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de834f71-a980-40a1-86f3-15d4c8b828c8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-90265528-d4ad-45fa-bbdb-5c11d2c470f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90265528-d4ad-45fa-bbdb-5c11d2c470f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f74ee3d-bd6e-4532-b33a-921cc12e013d' class='xr-var-data-in' type='checkbox'><label for='data-1f74ee3d-bd6e-4532-b33a-921cc12e013d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 70.25 70.5 ... 139.8 140.0</div><input id='attrs-b8c9fd23-9848-45f7-ba7f-9b3922d2ee17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8c9fd23-9848-45f7-ba7f-9b3922d2ee17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb377298-9b5b-4310-99b5-34f60eed93bc' class='xr-var-data-in' type='checkbox'><label for='data-cb377298-9b5b-4310-99b5-34f60eed93bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 70.  ,  70.25,  70.5 , ..., 139.5 , 139.75, 140.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 0.5 0.25 0.0</div><input id='attrs-3cf0ab5a-bd2a-4ba9-a524-501519f1fc50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cf0ab5a-bd2a-4ba9-a524-501519f1fc50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f03482b0-acf2-449a-89df-55f6712a5037' class='xr-var-data-in' type='checkbox'><label for='data-f03482b0-acf2-449a-89df-55f6712a5037' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , ...,  0.5 ,  0.25,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e684b655-05b1-4b9b-9494-c07d7fd41311' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e684b655-05b1-4b9b-9494-c07d7fd41311' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-822f6e35-2633-4800-a696-f4fce67816a3' class='xr-var-data-in' type='checkbox'><label for='data-822f6e35-2633-4800-a696-f4fce67816a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>69.875 0.25 0.0 60.125 0.0 -0.25</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad67ff1f-e4d1-46cd-ab47-99758e1b5532' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad67ff1f-e4d1-46cd-ab47-99758e1b5532' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cd06d65d-acb5-4855-9934-04612e63a396' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd06d65d-acb5-4855-9934-04612e63a396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9bb9cf3-4265-4ff2-a3ea-daf68959673e' class='xr-var-data-in' type='checkbox'><label for='data-d9bb9cf3-4265-4ff2-a3ea-daf68959673e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[67721 values with dtype=bool]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f882d227-ce5e-46f3-b1bf-49336a0de562' class='xr-section-summary-in' type='checkbox'  ><label for='section-f882d227-ce5e-46f3-b1bf-49336a0de562' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3cae74c-f670-4abb-9611-0d0d2382490e' class='xr-index-data-in' type='checkbox'/><label for='index-a3cae74c-f670-4abb-9611-0d0d2382490e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  70.0,  70.25,   70.5,  70.75,   71.0,  71.25,   71.5,  71.75,   72.0,\n",
       "        72.25,\n",
       "       ...\n",
       "       137.75,  138.0, 138.25,  138.5, 138.75,  139.0, 139.25,  139.5, 139.75,\n",
       "        140.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=281))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f5a8e80-3636-441d-9d28-0f5a4102770f' class='xr-index-data-in' type='checkbox'/><label for='index-5f5a8e80-3636-441d-9d28-0f5a4102770f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,   0.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7836e83-029c-43fe-acac-2395bf507cac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7836e83-029c-43fe-acac-2395bf507cac' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 70kB\n",
       "Dimensions:      (lon: 281, lat: 241)\n",
       "Coordinates:\n",
       "    time         datetime64[ns] 8B ...\n",
       "  * lon          (lon) float32 1kB 70.0 70.25 70.5 70.75 ... 139.5 139.8 140.0\n",
       "  * lat          (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.75 0.5 0.25 0.0\n",
       "    spatial_ref  int64 8B ...\n",
       "Data variables:\n",
       "    mask         (lat, lon) bool 68kB ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp = xr.open_dataset(r\"D:\\BC\\Data_experiment\\operational_bgg\\output_v01\\data\\mask_pearl_river.nc\")\n",
    "shp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x185a708bac0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shp['mask'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试调用代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f024.grib2.nc'),\n",
       " WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f048.grib2.nc'),\n",
       " WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f072.grib2.nc'),\n",
       " WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f096.grib2.nc'),\n",
       " WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f120.grib2.nc'),\n",
       " WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f144.grib2.nc'),\n",
       " WindowsPath('D:/BC/GFS.7z/GFS_real/gfs.0p25.2025010100.f168.grib2.nc')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "gfs_real_path = Path(r\"D:\\BC\\GFS.7z\\GFS_real\")\n",
    "gfs_files = list(gfs_real_path.glob('gfs.0p25.*.grib2.nc'))\n",
    "gfs_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f024.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 24小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
      "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f048.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 48小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
      "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f072.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 72小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
      "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f096.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 96小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
      "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f120.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 120小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
      "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f144.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 144小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
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      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "20250101\n",
      "处理文件: gfs.0p25.2025010100.f168.grib2.nc, 日期: 2025-01-01 00:00:00, 预见期: 168小时, 月份: 1\n",
      "<xarray.Dataset> Size: 8MB\n",
      "Dimensions:         (param: 4, lat: 241, lon: 281)\n",
      "Coordinates:\n",
      "  * lat             (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
      "  * lon             (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
      "  * param           (param) <U5 80B 'p0' 'a' 'loc' 'scale'\n",
      "Data variables:\n",
      "    para_fcst       (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    para_obs        (param, lat, lon) float64 2MB nan nan nan ... nan nan nan\n",
      "    corr            (lat, lon) float64 542kB nan nan nan nan ... nan nan nan nan\n",
      "    dist_type_fcst  (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n",
      "    dist_type_obs   (lat, lon) <U5 1MB '' '' '' '' '' '' ... '' '' '' '' '' ''\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import re\n",
    "\n",
    "def extract_info_from_filename(filename):\n",
    "    \"\"\"从文件名中提取日期和预见期信息\n",
    "    \n",
    "    Args:\n",
    "        filename (str): GFS预报文件名，例如 gfs.0p25.2025010100.f024.grib2\n",
    "        \n",
    "    Returns:\n",
    "        tuple: (日期对象, 预见期小时数, 月份)\n",
    "    \"\"\"\n",
    "    pattern = r'gfs\\.0p25\\.(\\d{10})\\.f(\\d{3})\\.grib2'\n",
    "    match = re.match(pattern, filename)\n",
    "    \n",
    "    if not match:\n",
    "        raise ValueError(f\"文件名格式不正确: {filename}\")\n",
    "    \n",
    "    date_str, lead_time_str = match.groups()\n",
    "    \n",
    "    # 解析日期\n",
    "    year = int(date_str[0:4])\n",
    "    month = int(date_str[4:6])\n",
    "    day = int(date_str[6:8])\n",
    "    hour = int(date_str[8:10])\n",
    "    \n",
    "    date = pd.Timestamp(year=year, month=month, day=day, hour=hour)\n",
    "    lead_time = int(lead_time_str)\n",
    "    \n",
    "    return date, lead_time, month\n",
    "\n",
    "def get_param_file(month, lead_time):\n",
    "    \"\"\"根据月份和预见期获取参数文件路径\n",
    "    \n",
    "    Args:\n",
    "        month (int): 月份 (1-12)\n",
    "        lead_time (int): 预见期小时数\n",
    "        \n",
    "    Returns:\n",
    "        Path: 参数文件路径\n",
    "    \"\"\"\n",
    "    param_file = param_path.joinpath(f'fitted_params_Month{month:02d}_Lead{lead_time:03d}.nc')\n",
    "    if not param_file.exists():\n",
    "        raise FileNotFoundError(f\"找不到参数文件: {param_file}\")\n",
    "    return param_file\n",
    "\n",
    "\n",
    "param_path = Path(r\"D:\\BC\\Data_experiment\\operational_bgg\\output_v01\\data\\daily_calibration_params\")\n",
    "\n",
    "for gfs_file in gfs_files:\n",
    "    # 从文件名提取信息\n",
    "    date, lead_time, month = extract_info_from_filename(gfs_file.name)\n",
    "    print(date.strftime('%Y%m%d'))\n",
    "    print(f\"处理文件: {gfs_file.name}, 日期: {date}, 预见期: {lead_time}小时, 月份: {month}\")\n",
    "\n",
    "    # 获取对应的参数文件\n",
    "    param_file = get_param_file(month, lead_time)\n",
    "    \n",
    "    # 读取参数文件\n",
    "    with xr.open_dataset(param_file) as ds_params:\n",
    "        params = ds_params.load()\n",
    "        print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  * ens_member        (ens_member) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
       "  * lat               (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, ens_member, lat, lon) float64 271MB ...\n",
       "    target_time       (time) datetime64[ns] 8B ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d071f861-3dda-4ad9-89f4-2faf80765e10' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d071f861-3dda-4ad9-89f4-2faf80765e10' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>ens_member</span>: 500</li><li><span class='xr-has-index'>lat</span>: 241</li><li><span class='xr-has-index'>lon</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c88aa94e-4119-457c-b49f-2edaaf49ef68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c88aa94e-4119-457c-b49f-2edaaf49ef68' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-01-01</div><input id='attrs-7786803a-ecdb-4bc7-bba0-eaa0aceaaf04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7786803a-ecdb-4bc7-bba0-eaa0aceaaf04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cfbd9f4-54be-4090-b34d-c8ef330e3521' class='xr-var-data-in' type='checkbox'><label for='data-9cfbd9f4-54be-4090-b34d-c8ef330e3521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ens_member</span></div><div class='xr-var-dims'>(ens_member)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-47fdc581-2785-4138-beb3-fe54f1901833' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47fdc581-2785-4138-beb3-fe54f1901833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47d69b0-f047-4e15-9be1-0285024d69ac' class='xr-var-data-in' type='checkbox'><label for='data-a47d69b0-f047-4e15-9be1-0285024d69ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 0.5 0.25 0.0</div><input id='attrs-1b8c3863-b8ce-4e3b-ad36-ace4600cddfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b8c3863-b8ce-4e3b-ad36-ace4600cddfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bb3a851-8e29-4ab7-a008-5e463979f6f4' class='xr-var-data-in' type='checkbox'><label for='data-8bb3a851-8e29-4ab7-a008-5e463979f6f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , ...,  0.5 ,  0.25,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 70.25 70.5 ... 139.8 140.0</div><input id='attrs-b0c41d62-cf8e-456b-91b0-df9018f26f01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c41d62-cf8e-456b-91b0-df9018f26f01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bca52d94-8725-44f8-b260-f30e763d01e9' class='xr-var-data-in' type='checkbox'><label for='data-bca52d94-8725-44f8-b260-f30e763d01e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 70.  ,  70.25,  70.5 , ..., 139.5 , 139.75, 140.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-493a37d5-1319-4dd1-ac16-c03a24d5c967' class='xr-section-summary-in' type='checkbox'  checked><label for='section-493a37d5-1319-4dd1-ac16-c03a24d5c967' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>A_PCP_L1_Accum_1</span></div><div class='xr-var-dims'>(time, ens_member, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-896c8df8-b8aa-4c73-84d6-49a28d9976a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-896c8df8-b8aa-4c73-84d6-49a28d9976a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b0b949c-481b-4dd5-9c8b-ea2b203f11f9' class='xr-var-data-in' type='checkbox'><label for='data-1b0b949c-481b-4dd5-9c8b-ea2b203f11f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[33860500 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da09fa9d-61c7-46f9-87a8-b3065a745679' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da09fa9d-61c7-46f9-87a8-b3065a745679' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905d2ce6-d616-4988-820a-f91b64f62df6' class='xr-var-data-in' type='checkbox'><label for='data-905d2ce6-d616-4988-820a-f91b64f62df6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d68437b4-c369-4033-aa11-2c6017982cdc' class='xr-section-summary-in' type='checkbox'  ><label for='section-d68437b4-c369-4033-aa11-2c6017982cdc' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ef1bcb18-e2db-445d-9b07-3c2721ab3a49' class='xr-index-data-in' type='checkbox'/><label for='index-ef1bcb18-e2db-445d-9b07-3c2721ab3a49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2025-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ens_member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08f896c7-329a-42a3-bcb8-6455f5a35370' class='xr-index-data-in' type='checkbox'/><label for='index-08f896c7-329a-42a3-bcb8-6455f5a35370' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;ens_member&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-91374d45-2356-4dd4-ab49-011b5f89193f' class='xr-index-data-in' type='checkbox'/><label for='index-91374d45-2356-4dd4-ab49-011b5f89193f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,   0.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7517f5be-b0b7-476c-ba08-0bff5522e540' class='xr-index-data-in' type='checkbox'/><label for='index-7517f5be-b0b7-476c-ba08-0bff5522e540' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  70.0,  70.25,   70.5,  70.75,   71.0,  71.25,   71.5,  71.75,   72.0,\n",
       "        72.25,\n",
       "       ...\n",
       "       137.75,  138.0, 138.25,  138.5, 138.75,  139.0, 139.25,  139.5, 139.75,\n",
       "        140.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=281))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c95281ba-7072-4e95-92d2-1b137d22e444' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c95281ba-7072-4e95-92d2-1b137d22e444' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 271MB\n",
       "Dimensions:           (time: 1, ens_member: 500, lat: 241, lon: 281)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 8B 2025-01-01\n",
       "  * ens_member        (ens_member) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
       "  * lat               (lat) float32 964B 60.0 59.75 59.5 59.25 ... 0.5 0.25 0.0\n",
       "  * lon               (lon) float32 1kB 70.0 70.25 70.5 ... 139.5 139.8 140.0\n",
       "Data variables:\n",
       "    A_PCP_L1_Accum_1  (time, ens_member, lat, lon) float64 271MB ...\n",
       "    target_time       (time) datetime64[ns] 8B ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_cal = xr.open_dataset(r\"D:\\BC\\Data_experiment\\operational_bgg\\output_v01\\data\\calibrated_forecasts\\48\\gfs.cal.0p25.2025010100.f048.grib2.nc\")\n",
    "da_cal"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "gfs2025",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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